{"type":"ScholarlyArticle","title":"Multi-view Clustering via Late Fusion Alignment Maximization","authors":[{"name":"Siwei Wang","slug":"siwei-wang","given":"Siwei","family":"Wang","affiliation":"College of Computer , National University of Defense Technology , Changsha , China"},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":"College of Computer , National University of Defense Technology , Changsha , China"},{"name":"En Zhu","slug":"en-zhu","given":"En","family":"Zhu","affiliation":"College of Computer , National University of Defense Technology , Changsha , China"},{"name":"Chang Tang","slug":"chang-tang","given":"Chang","family":"Tang","affiliation":"School of Computer Science , China University of Geosciences , Wuhan , China"},{"name":"Jiyuan Liu","slug":"jiyuan-liu","given":"Jiyuan","family":"Liu","affiliation":"College of Computer , National University of Defense Technology , Changsha , China"},{"name":"Jingtao Hu","slug":"jingtao-hu","given":"Jingtao","family":"Hu","affiliation":"College of Computer , National University of Defense Technology , Changsha , China"},{"name":"Jingyuan Xia","slug":"jingyuan-xia","given":"Jingyuan","family":"Xia","affiliation":"Department of Electric and Electronic Engineering , Imperial College London"},{"name":"Jianping Yin","slug":"jianping-yin","given":"Jianping","family":"Yin","affiliation":"School of Cyberspace Science , Dongguan University of Technology , Guangdong 523808 , China"}],"abstract":"Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.","doi":"10.24963/ijcai.2019/524","url":"https://ijcai.org/proceedings/2019/524","pdf":"https://ijcai.org/proceedings/2019/0524.pdf","markdown":"https://ijcai.org/proceedings/2019/524.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/524","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":524,"pages":{"first":3778,"last":3784},"keywords":["Machine Learning: Clustering","Machine Learning: Kernel Methods","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Boutsidis et al., 2015] Christos Boutsidis, Anastasios Zouzias, Michael W Mahoney, and Petros Drineas. 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